We formalize a model of "demonstration of program result-correctness," and investigate how to prove this fact against possible adversaries, which naturally extends Blum's theory of program checking by adding zero-knowledge requirements. The zero-knowledge requirements are universal for yes and no instances alike.

In this paper, we propose a mathematical model for one-dimensional finite linear cellular automata and show connections between our model and the classical one. We then demonstrate, through some examples, that our model is a useful tool for analyzing one-dimensional finite linear cellular automata. We also extend this model to the D-dimensional case and give an algebraic characterization for it.

A uniquely parsable unification grammar (UPUG) is a formal grammar with the following features: (1) parsing is performed without backtracking, and (2) each nonterminal symbol can have arguments, and derivation and parsing processes accompany unification of terms as in Prolog (or logic programming). We newly introduce a uniquely parallel parsable unification grammar (UPPUG) by extending the framework of a UPUG so that parallel parsing is also possible. We show that, in UPPUG, parsing can be done without backtracking in both cases of parallel and sequential reductions. We give examples of UPPUGs where a given input string can be parsed in sublinear number of steps of the length of the input by parallel reduction.

In this paper, we consider the complexity of recognizing ordered tree-shellable Boolean functions when Boolean functions are given as OBDDs. An ordered tree-shellable function is a positive Boolean function such that the number of prime implicants equals the number of paths from the root node to a 1-node in its ordered binary decision tree representation. We show that given an OBDD, it is possible to check within polynomial time if the function is ordered tree-shellable with respect to the variable ordering of the OBDD.

We introduce a refined definition of semantic security. The new definition is valid against not only chosen-plaintext attacks but also chosen-ciphertext attacks whereas the original one is defined against only chosen-plaintext attacks. We show that semantic security formalized by the new definition is equivalent to indistinguishability, due to Goldwasser and Micali for each of chosen-plaintext attacks, non-adaptive chosen-ciphertext attack, and adaptive chosen-ciphertext attack.

We present a distributed protocol for achieving totally unbiased global coin flipping in the presence of an adversary. We consider a synchronous system of n processors at most t of which may be corrupted and manipulated by a malicious adversary, and assume a complete network where every two processors are connected via a private channel. Our protocol is deterministic and assumes a very powerful adversary. Although the adversary cannot eavesdrop, it is computationally unbounded, capable of rushing and dynamic. This is the same model that is adopted in Yao's global coin flipping protocol, which we use as the base of our protocol. Our protocol tolerates almost n/3 processor failures and terminates in t+4 rounds. The resilience of our protocol is greatly improved from that of Yao's protocol at the slight expense of running time, which is only added just two rounds.

We present a new method for discovering knowledge from structured data which are represented by graphs in the framework of Inductive Logic Programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces, and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KD-FGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.

This paper shows that nondeterministic sensing semi-one-way simple k-head finite automata are more powerful than nondeterministic sensing one-way simple k-head finite automata for any k2, and sensing semi-one-way simple 2-head finite automata are more powerful than semi-one-way simple 2-head finite automata, which gives an affirmative answer and a partial solution to two open problems on sensing semi-one-way simple multi-head finite automata in Ref.[3].

This paper investigates a hierarchical property based on the number of inkdots in the accepting powers of sublogarithmic space-bounded multi-inkdot two-way alternating Turing machines with only universal states. For each k1 and any function L(n), let strong-2UTM^k(L(n)) (weak-2UTM^k(L(n))) be the class of sets accepted by strongly (weakly) L(n) space-bounded k-inkdot two-way alternating Turing machines with only universal states. We show that for each k1, strong-2UTM^k+1(log log n) - weak-2UTM^k(o(log n)) Ø.

We study the performance of oblivious routing algorithms that follow minimal (shortest) paths, referred to as minimal oblivious routing algorithms in this paper, using competitive analysis on a d-dimensional, N = 2^d-node hypercube. We assume that packets are injected into the hypercube arbitrarily and continuously, without any (e.g., probabilistic) assumption on the arrival pattern of the packets. Minimal algorithms reduce the total load in the network in the first place and they preserve locality. First we show that the well known deterministic oblivious routing algorithm, namely, the greedy routing algorithm, has competitive ratio Ω(N^1/2). Then we show a problem lower bound of Ω(N^{log 2 (5/4)}/log⁵ N). We also give a natural randomized minimal oblivious routing algorithm whose competitive ratio is close to the problem lower bound we provide.

In this paper we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. Moving average transform reduces the effect of noise and has been used in many areas such as econometrics since it is useful in finding the overall trends. The proposed algorithm extends the existing subsequence matching algorithm proposed by Faloutsos et al. (SUB94 in short). If we applied the algorithm without any extension, we would have to generate an index for each moving average order and would have serious storage and CPU time overhead. In this paper we tackle the problem using the notion of index interpolation. Index interpolation is defined as a searching method that uses one or more indexes generated for a few selected cases and performs searching for all the cases satisfying some criteria. The proposed algorithm, which is based on index interpolation, can use only one index for a pre-selected moving average order k and performs subsequence matching for arbitrary order m ( k). We prove that the proposed algorithm causes no false dismissal. The proposed algorithm can also use more than one index to improve search performance. The algorithm works better with smaller selectivities. For selectivities less than 10^-2, the degradation of search performance compared with the fully-indexed case--which is equivalent to SUB94--is no more than 33.0% when one index is used, and 17.2% when two indexes are used. Since the queries with smaller selectivities are much more frequent in general database applications, the proposed algorithm is suitable for practical situations.

There are more and more information services provided on the wireless networks. Due to long network delay of wireless links, transactions will be long-lived transactions. In such a situation, the occurrence of handoff is inevitable, and thus a wireless link held by a mobile unit crossing cell boundaries might be forced to terminate. It is undesirable that an active transaction is forced to terminate. A queueing scheme has been proposed to solve the problem of forced termination of transactions in our previous research. However, when 2PL protocol is employed, suspending an active transaction will elongate the lock holding time and thus degrade the system performance. In this paper, we propose two guard channel schemes (GCS), static and dynamic, to reduce the probability of forced termination of transactions. In dynamic GCS, the number of channels reserved in a base station is dynamically assigned according to the number of transaction calls which may handoff to this cell while the number of guard channels is fixed in static GCS. An analytic model based on Markov chain is derived to evaluate the system performance. The correctness of this model is verified by simulation. The experimental results show that a significant improvement is achieved by using the dynamic GCS.

Semistructured data has no a-priori schema information, which causes some problems such as inefficient storage and query execution. To cope with such problems, extracting schema information from semistructured data has been an important issue. However, in most cases optimal schema information cannot be extracted efficiently, and few efficient approximation algorithms have been proposed. In this paper, we consider an approximation algorithm for extracting "typical" classes from semistructured data. Intuitively, a class C is said to be typical if the structure of C is "similar" to those of "many" objects. We present the following results. First, we prove that the problem of deciding if a typical class can be extracted from given semistructured data is NP-complete. Second, we present an approximation algorithm for extracting typical classes from given semistructured data, and show a sufficient condition for the approximation algorithm to run in polynomial time. Finally, by using extracted classes obtained by the approximation algorithm, we propose a polynomial-time algorithm for constructing a set R of classes such that R covers all the objects to form a database schema.

In recent years, more emphasis is placed on the performance of massive databases. It is often required not only that database systems provide high throughputs with rapid response times, but also that they are fully available 24-hours-per-day and 7-days-per-week. Requirements for throughput and response time can be satisfied by upgrading the hardware. As a result, databases in the old hardware environment have to be moved to the new one. Moving a database, however, generally requires taking the database off line for a long time, which is unacceptable for numerous applications. In this paper, a very practical and important subject is addressed: how to upgrade the hardware on line, i.e., how to move a database from an old hardware environment to a new one concurrently with users' reading and writing of the database. A technique for this purpose is proposed. We have implemented a prototype based on this technique. Our experiments with the prototype shown that compared with conventional off-line approach, the proposed technique could give a performance improvement by more than 85% in the query-bound environment and 40% in the update-bound environment.

Many researchers have used hypercube interconnection networks for their good properties to construct many parallel processing systems. However, as the number of processors increases, the probability of occurrences of faulty nodes also increases. Hence, for hypercube interconnection networks which have faulty nodes, several efficient dynamic routing algorithms have been proposed which allow each node to hold status information of its neighbor nodes. In this paper, we propose an improved version of the algorithm proposed by Chiu and Wu by introducing the notion of full reachability. A fully reachable node is a node that can reach all nonfaulty nodes which have Hamming distance l from the node via paths of length l. In addition, we further improve the algorithm by classifying the possibilities of detours with respect to each Hamming distance between current and target nodes. We propose an initialization procedure which makes use of an equivalent condition to perform this classification efficiently. Moreover, we conduct a simulation to measure the improvement ratio and to compare our algorithms with others. The simulation results show that the algorithms are effective when they are applied to low-dimensional hypercube interconnection networks.

Modeling a complicated system as a multi-agent system is one of the most promising ways of designing a large, complex system. If we can assume that each agent in a multi-agent system has mental states (beliefs, knowledge, desires and so on), we can formalize each agent's behaviors in an abstract way without being bothered by system implementation details. We present semantic structures that are useful for representing belief states in multi-agent environments. One of the structures is a restriction of partial Kripke structures studied by Jaspars and Thijsse: we assume that each agent can access from a state of a structure to at most one state. We call the restricted structures only-child partial Kripke structures. We show some properties of only-child partial Kripke structures. Another structure is a restriction of the alternate nonstandard structures defined by Fagin et al. to deal with the logical-omniscience problem. We show several relationships between partial Kripke structures and the restriction of alternate nonstandard structures. Using the results, we show that the outputs of a belief estimation algorithm we previously developed can be characterized by using only-child partial Kripke structures. Finally, we show that only-child partial Kripke structures are more appropriate for the belief estimation problem than the restricted nonstandard structures.

This paper describes an application of a speech translation system to another task/domain in the real-world by using developmental data collected from real-world interactions. The total cost for this task-alteration was calculated to be 9 Person-Month. The newly applied system was also evaluated by using speech data collected from real-world interactions. For real-world speech having a machine-friendly speaking style, the newly applied system could recognize typical sentences with a word accuracy of 90% or better. We also found that, concerning the overall speech translation performance, the system could translate about 80% of the input Japanese speech into acceptable English sentences.

This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.

In this paper, we present a computer-aided diagnosis (CAD) system to automatically detect lung cancer candidates at an early stage using a present and a past helical CT screening. We have developed a slice matching algorithm that can automatically match the slice images of a past CT scan to those of a present CT scan in order to detect changes in the lung fields over time. The slice matching algorithm consists of two main process: the process of extraction of the lungs, heart, and descending aorta and the process of matching slices of the present and past CT images using the information of the lungs, heart, and descending aorta. To evaluate the performance of this algorithm, we applied it to 50 subjects (total of 150 scans) screened between 1993 and 1998. From these scans, we selected 100 pairs for evaluation (each pair consisted of scans for the same subject). The algorithm correctly matched 88 out of the 100 pairs. The slice images for the present and past CT scans are displayed in parallel on the CRT monitor. Feature measurements of the suspicious regions are shown on the relevant images to facilitate identification of changes in size, shape, and intensity. The experimental results indicate that the CAD system can be effectively used in clinical practice to increase the speed and accuracy of routine diagnosis.

This paper presents a new data hiding scheme under fractal image generation via Fourier filtering method for Computer Graphics (CG) applications. The data hiding operations are achieved in the frequency domain and a method similar to QAM used in digital communication is introduced for efficient embedding in order to explore both phase and amplitude components simultaneously. Consequently, this scheme enables us not only to generate a natural terrain surface without loss of fractalness analogous to the conventional scheme, but also to embed larger amounts of data into an image depending on the fractal dimension. This scheme ensures the correct decoding of the embedded data under lossy data compression such as JPEG by controlling the quantization exponent used in the embedding process.

This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multi-objects in occlusion and maneuvering, when compared to other conventional trackers such as Kalman filter.

In this paper, we propose a realtime concatenation technique between basic skeletal motions obtained by the motion capture technique and etc. to generate a lifelike behavior for a humanoid character (avatar). We execute several experiments to show the advantage and the property of our technique and also report the results. Finally, we describe our applied system called WonderSpace which leads participants to the exciting and attractive virtual worlds with humanoid characters in cyberspace. Our concatenation technique has the following features: (1) based on a blending method between a preceding motion and a succeeding motion by a transition function, (2) realizing "smooth transition," "monotone transition," and "equivalent transition" by the transition function called paste function, (3) generating a connecting interval by making the backward and forward predictions for the preceding and succeeding motions, (4) executing the prediction under the hypothesis of "the smooth stopping state" or "the state of connecting motion", (5) controlling the prediction intervals by the parameter indicating the importance of the motion, and (6) realizing realtime calculation.

The first important step in pre-processing data for 3D virtual colonoscopy requires careful segmentation of a complicated shaped colon. We describe an automatic colon segmentation method with a new patient-friendly bowel preparation scheme. This new bowel preparation makes the segmentation more appropriate for digitally removing undesirable remains in the colon. With the aim of segmenting the colon accurately, we propose two techniques which can solve the partial-volume-effect (PVE) problem on the boundaries between low and high intensity regions. Based on the features of the adverse PVE voxels on the gas and fluid boundary inside the colon, our vertical filter eliminates these PVE voxels. By seriously considering the PVE on the colon boundary, our gradient-magnitude-based region growing algorithm improves the accuracy of the boundary. The result of the automatic colon segmentation method is illustrated with both extracted 2D images from the experimental volumetric abdominal CT datasets and a reconstructed 3D colon model.

We propose a blind source separation algorithm for the mixture of finite alphabet sources where sensors are less than sources. The algorithm consists of an update equation of an estimated mixing matrix and enumeration of the inferred sources. We present the bound of a step size for the stability of the algorithm and two methods of assignment of the initial point of the estimated mixing matrix. Simulation results verify the proposed algorithm.

This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning method, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear combination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.